Recently significant progress has been made in vehicle prediction and planning algorithms
for autonomous driving. However, it remains quite challenging for an autonomous vehicle to …

The reasonable trajectory prediction of surrounding traffic participants is crucial for
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …

Integrating trajectory prediction to the decision-making and planning modules of modular
autonomous driving systems is expected to improve the safety and efficiency of self-driving …

Simultaneous trajectory prediction for multiple heterogeneous traffic participants is essential
for safe and efficient operation of connected automated vehicles under complex driving …

In this paper, we develop a safe decision-making method for self-driving cars in a multi-lane,
single-agent setting. The proposed approach utilizes deep reinforcement learning (RL) to …

In recent years, the expansion of internet technology and advancements in automation have
brought significant attention to autonomous driving technology. Major automobile …

Interactive driving is challenging but essential for autonomous cars in dense traffic or urban
areas. Proper interaction requires understanding and prediction of future trajectories of all …

The trajectory prediction is significant for the decision-making of autonomous driving
vehicles. In this paper, we propose a model to predict the trajectories of target agents around …

Predicting the behavior of surrounding traffic participants is crucial for advanced driver
assistance systems and autonomous driving. Most researchers however do not consider …

Accurate vehicle trajectory prediction can benefit a variety of intelligent transportation system
applications ranging from traffic simulations to driver assistance. The need for this ability is …